Image processing techniques are important to many applications. For example, image restoration, in which an unknown image is recovered from a degraded version of itself, may utilize various image processing techniques to restore the unknown image. Despite its usefulness for recovering lost image features, image restoration presents significant challenges. Specifically, for instance, the problems posed by image restoration tend to be inherently underdetermined because multiple plausible images can be recovered from the same degraded original image.
Due to its underdetermination, image restoration requires some prior information about the image undergoing restoration. Traditional approaches to obtaining such prior information have been variously based on edge statistics, sparse representation, gradients, self-similarities, or some combination of those features. Nevertheless, there remains a need in the art for an image processing solution capable of using non-traditional forms of prior information to more effectively guide the accurate restoration or enhancement of a degraded or corrupted image.